I am a Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech). Before joining SUSTech in January 2018, I was with the School of Computer Science and Technology, University of Science and Technology of China (USTC), first as an Associate Professor (2007-2011) and then as a Professor (2011-2017). My research interests include fundamental issues of Evolutionary Computation and Machine Learning, as well as applied AI techniques for industrial design, finance and smart logistics.
I am a recipient of the IEEE Computational Intelligence Society Outstanding Early Career Award and the Natural Science Award of Ministry of Education (MOE) of China, and was awarded the Newton Advanced Fellowship (Royal Society) and the Changjiang Professorship (MOE of China).
Employment
- Jan. 2018 – present: Professor, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong, China.
- Feb. 2011 – Dec. 2017: Professor, School of Computer Science and Technology, USTC.
- Jun. 2007 – Jan. 2011: Associate professor, School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, P. R. China.
Education
- Ph. D (2007), School of Electrical and Electronic Engineering, Nanyang Technological University, Republic of Singapore.
- B. Eng (2002), Department of Control Science and Engineering, Huazhong University of Science and Technology, China.
Awards and Honors
- 2021: Changjiang Professorship, Ministry of Education of China
- 2019: National Leading Youth Talent Support Program of China
- 2018: IEEE Computational Intelligence Society Outstanding Early Career Award
- 2017: Natural Science Award (1st prize) of Ministry of Education of China
- 2015: Royal Society Newton Advanced Fellowship (2016-2018)
- 2015: Natural Science Award (1st prize) of The Chinese Institute of Electronics
- 2014: Outstanding Paper Award, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2014), November 10-12, Singapore, for the following paper:
Z. Miao, J. Wang, A. Zhou and K. Tang, “Regularized Boost for Semi-supervised Ranking,” in Proceedings of IES2014, Volume 1, Proceedings in Adaptation, Learning and Optimization Volume 1, 2015, pp 643-651. - 2014: The China Institute Joint Li Siguang Sino-UK Publication Prize 2014, The University of Birmingham, for the following paper:
M. Lin, K. Tang and X. Yao, “Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification,” IEEE Transactions on Neural Networks and Learning Systems, 24(4): 647-660, April 2013. - 2012: New Century Excellent Talent, Ministry of Education of China
- 2011: Natural Science Award (2nd prize) of Ministry of Education of China
- 2009: Young Faculty Career Award of the University of Science and Technology of China, awarded by the USTC Alumni Foundation.
- 2005: Best Student Paper Award at the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, California, USA, 14-15, November, 2005.
Services
- Associate Editor, IEEE Transactions on Evolutionary Computation
- Associate Editor, Swarm and Evolutionary Computation
- Program/technical chair of a number of academic conferences, including IEEE-CEC, SEAL, IDEAL etc.
- Founding chair of the Chinese Workshop on Evolutionary COmputation and LEarning (ECOLE)
Research
My research is, in general, about fundamental issues of computational approaches for Learning and Optimization, two most important problems in Artificial Intelligence. I’m also frequently attracted by other relevant domains, such as Smart Logistics, Structural and Multi-Disciplinary Optimization and Computational Finance, for which applied research is required to produce application-oriented learning and optimization techniques.
Most of my research could also be viewed as arising from Evolutionary Computation, which is essentially a distributed heuristic search framework widely applicable for modelling, learning and optimization problems, especially for hard problems where limited prior knowledge is available.
Selected topics:
Scalable Evolutionary Search
Research in this direction aims at systematically boosting the capacity of Evolutionary Computation on problems with huge search space, which has been believed as a major challenge for most EAs. Approaches for this purpose include:
- Co-evolutionary Search: Introducing the divide-and-conquer idea to guide EAs adaptively search different regions of the search space, see Cooperative Coevolution (CC) and Negatively Correlated Search (NCS) for examples.
- Parallel Algorithm Portfolios: Leveraging on high performance computing to enhance both the extreme performance and reliability of EAs, without suffering the wall-clock runtime but only computational resources. See PCIT and CEPS for examples.
- Surrogate-assisted Search: Exploiting data generated during the search course to alleviate the cost of evaluating a future solution, see examples here.
Reinforcement and Evolutionary Learning
Reinforcement Learning is a learning problem that lies exactly in the “backyard” of EAs, because the objective function of most RL tasks so far rely on a noisy and non-differentiable simulator. Thus it’d be quite interesting to see whether EC could offer a promising alternative approach for RL. Some preliminary results confirming this hypothesis could be found here.
Learning and Optimization with Uncertainty
Uncertainty is ubiquitous in real-world learning and optimization tasks. It could be due to the dynamically changing physical world, the noise caused by imprecise measurements, or even the unpredictable nature of human behaviors. We are specifically interested in new learning/optimization methods that could handle various forms of uncertainty. This has led to exploration on the following topics:
- Incremental learning with concept drift
- Evolutionary computation for Dynamic optimization
- Learning from crowds (Crowdsourcing Learning)
Codes
- For general purpose continuous black-box optimization
- For Capacitated Arc/Vehicle Routing
- For Deep Neural Network Compression
- For Competitive Influence Maximization